Integrity Constraints in Spatial Databases

Borges, Karla A.V.; Davis, Clodoveu A.; Laender, Alberto H. F.

doi:10.4018/9781930708389.ch005

Cited by 5 publications

(11 citation statements)

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“…In a tree-dimensional space, a polyhedra is represented by the boundaries of which contain planar facets (i.e., surfaces), polylines, and points. B A (3,9) (1,5) (3,5) [5,7], [3,5], [1,5], [3,9] > B = < [5,7], [8,7], [5,1], [3,5], [5,7] > Other types of models that concern with practical issues of efficiency are the raster model and the piano model [38] [39] [55], which are often, but not always, seen as the typical way to model a field view of the space. The raster model intentionally represents spatial information by a finite number of cells or raster points, where the infinite number of points associated with a cell share the same properties.…”

Section: Spatial Data Models or Geomatic Modelsmentioning

confidence: 99%

“…The difference between the two models is that the topological model handles explicitly common boundaries and adjacency between polygons. [5,7], [3,5], [1,5], [3,9] [8,7], [5,1], [3,5], [5,7] The constraint model defines any geometrical figure by an elementary geometry expressed by first-order logic over the real numbers [42]. The constraint data model aims to handle infinite relations (i.e., infinite sets of points in a space), which are represented by quantifier-free formulas.…”

Section: Spatial Data Models or Geomatic Modelsmentioning

confidence: 99%

“…These types of inconsistency, structural or semantic, depend on the spatial domain, and they are captured by rules that should be expressed within the data model. Some relevant characteristics of spatial applications that should be considered in the treatment of consistency are [8] [53]:…”

Section: Types and Sources Of Inconsistency Of Spatial Informationmentioning

confidence: 99%

“…Although there has been active research on creating efficient spatial databases, the treatment of inconsistency in spatial databases is still a problem for current spatial information systems [8] [23]. The models described in the previous Sections about consistency at multiple representational levels and for data integration can be used in defining strategies for treating inconsistency in spatial databases; however, these models have not been integrated into a query process that explicitly addresses the answer and process of data despite the fact that the data are inconsistent (i.e., inconsistency tolerance).…”

Section: Consistency Tolerance In Spatial Databasesmentioning

confidence: 99%

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Inconsistency Issues in Spatial Databases

Rodrı́guez

2005

Inconsistency Tolerance

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Abstract. This chapter analyzes inconsistency issues in spatial databases. In particular, it reviews types of inconsistency, specification of integrity constraints, and treatment of inconsistency in multiple representations and data integration. The chapter focuses on inconsistency associated with the geometric representation of objects, spatial relations between objects, and composite objects by aggregation. The main contribution of this paper is a survey of existing approaches to dealing with inconsistency issues in spatial databases that emphasizes the current state of the art and that outlines research issues in the context of inconsistency tolerance.

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Section: Spatial Data Models or Geomatic Modelsmentioning

confidence: 99%

Section: Spatial Data Models or Geomatic Modelsmentioning

confidence: 99%

Section: Types and Sources Of Inconsistency Of Spatial Informationmentioning

confidence: 99%

Section: Consistency Tolerance In Spatial Databasesmentioning

confidence: 99%

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Inconsistency Issues in Spatial Databases

Rodrı́guez

2005

Inconsistency Tolerance

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“…In terms of data integration, spatial relations are often used to build integrity constraints that will control the integration process (Borges et al, 2002).…”

Section: Properties and Relations In Urban Applicationsmentioning

confidence: 99%

3D City Models and urban information: Current issues and perspectives

Billen¹,

Cutting-Decelle²,

Marina³

et al. 2014

3D City Models and Urban Information: Current Issues and Perspectives – European COST Action TU0801

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Rank-based strategies for cleaning inconsistent spatial databases

Brisaboa

Rodríguez

Seco

et al. 2014

International Journal of Geographical Information Science

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A spatial data set is consistent if it satisfies a set of integrity constraints. Although consistency is a desirable property of databases, enforcing the satisfaction of integrity constraints might not be always feasible. In such cases, the presence of inconsistent data may have a negative effect on the results of data analysis and processing and, in consequence, there is an important need for data-cleaning tools to detect and remove, if possible, inconsistencies in large data sets. This work proposes strategies to support data cleaning of spatial databases with respect to a set of integrity constraints that impose topological relations between spatial objects. The basic idea is to rank the geometries in a spatial data set that should be modified to improve the quality of the data (in terms of consistency). An experimental evaluation validates the proposal and shows that the order in which geometries are modified affects both the overall quality of the database and the final number of geometries to be processed to restore consistency.

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Integrity Constraints in Spatial Databases

Cited by 5 publications

References 0 publications

Inconsistency Issues in Spatial Databases

Inconsistency Issues in Spatial Databases

3D City Models and urban information: Current issues and perspectives

Rank-based strategies for cleaning inconsistent spatial databases

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